Data Engineer I

RevSpring•Boston, MA

19h

About The Position

This role supports the design and implementation of changes to our data model in collaboration with senior data engineers and the product team. The Data Engineer I will develop, maintain, and update dashboards and reports using Looker with guidance. They will actively participate in team meetings, submit clear pull requests, respond to code review feedback, and learn team standards. The position involves troubleshooting data-related issues with support, participating in the team’s on-call rotation, and contributing to reliable customer-facing analytic services by completing well-scoped tasks. The role requires delivering assigned work on committed business initiatives, communicating progress, risks, and blockers clearly, and helping maintain data pipelines, dashboards, and related infrastructure. The Data Engineer I will support business teams and customers by improving access to accurate analytic insights, produce high-quality code and documentation, and contribute positively to a team culture where learning, accountability, and collaboration thrive.

Requirements

Ability to approach well-defined data challenges systematically, ask clarifying questions, and follow through on assigned work.
Foundational understanding of data quality concepts, including reliable, timely, and traceable data from source to consumption.
Foundational experience with SQL databases or data warehouse technologies such as Postgres, BigQuery, Redshift, or similar platforms.
A fast learner who is excited to build analytic dashboards, learn modern data engineering practices, and contribute to data pipeline improvements.
Ability to communicate technical issues, progress, and blockers clearly to teammates and stakeholders.
Coursework, internship, project, or professional exposure to data modeling concepts.
Exposure to the US healthcare system, health systems, or payers.
Knowledge of data pipeline concepts and basic data modeling principles.
Familiarity with analytic semantic layers and business intelligence dashboard tools such as Looker, Power BI, Tableau, or similar tools.
Familiarity with cloud services such as AWS, Azure, or GCP.
Familiarity with git and command-line tools.
Exposure to ETL or ELT pipelines for analytics.
Interest in leveraging AI agents and other AI tools to enhance developer productivity.
BS Degree or equivalent work experience
0–2 years of experience, including relevant internships, coursework, bootcamps, projects, or professional experience.
Must have foundational experience with Python and SQL and an interest in data engineering, analytics engineering, or data science.
Ability to read, analyze and interpret general business periodicals, professional journals, technical procedures or governmental regulations.
Ability to write reports, business correspondence and procedure manuals.
Ability to effectively present information and respond to questions from a variety of both internal and external sources.

Responsibilities

Support the design and implementation of changes to our data model in collaboration with senior data engineers and the product team.
Develop, maintain, and update dashboards and reports using our BI tool, Looker, with guidance from the team.
Actively participate in team meetings, including stand-ups, planning sessions, and retrospectives.
Submit clear pull requests, respond to code review feedback, and learn team standards for maintainable data engineering work.
Troubleshoot data-related issues with support from more experienced engineers, helping ensure data integrity and reliability.
Participate in the team’s on-call rotation and be responsible for monitoring our systems and debug issues with support from the team.
Contribute to reliable customer-facing analytic services by completing well-scoped tasks with guidance.
Deliver assigned work on committed business initiatives and communicate progress, risks, and blockers clearly.
Help maintain data pipelines, dashboards, and related infrastructure so team service expectations are met.
Support business teams and customers by improving access to accurate analytic insights.
Produce high-quality code and documentation that follow team standards and require appropriate review from more experienced engineers.
Contribute positively to a team culture where learning, accountability, and collaboration thrive.